51–60 of 84 posts
How does the model qualify as local? ~192 GB RAM needed sounds a bit much for local.
Bit like asking if Zigbee can be considered local/LAN for people who don't have the required radio/antenna.
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M2.7 was never open source, only open weight, which fulfills a lot of the spirit of open source, but isn't really the same thing as a whole. The noncommercial license is basically impossible to enforce if you're self-hosting anyway, because it's essentially impossible to prove that any individual commit was made by Minimax M2.7 in an environment where multiple self-hosted models are being run side-by-side. Besides th…
As to the 2nd part of your message, it's really easy to verify yourself (on openrouter). DSv4-flash is currently being served at 0.14/0.24 $/MTok by most of the providers (8 as of writing this) and even a bit cheaper by 2 providers. Minimax2.7 is being served at 0.30/1.20 $/MTok by most providers (4 providers as of writing this) and double that price by 2 providers. As for the first part of your message, this is actu…
I am currently serving Minimax M2.7 to myself at ~$0.015/1M blended tokens worth of electricity on my own local hardware, where I get all of the confidentiality, integrity, and availability benefits that are lost when choosing to run open weight models on someone else's API.
Open source means that all of the information necessary to recreate the final product is public, which in the context of LLMs, would include all of the training material, and build instructions (scripts to do the training). Very few models actually achieve this - Nemotron family is the only one that comes top of mind. A license to run, inspect, modify, and re-release is a good improvement on open weight models, but does not alone amount to the model actually being open source.
You are welcome to an alternative understanding of the definition of open source - as you correctly note, it's a contested term - just know that your definition is not the more widely accepted one that people think of when they hear "open source".
Your version of the term is much more aligned with the OSI, which was a federation of anti-FLOSS industry bodies created with the intent to capture, redefine, and weaken the original spirit of the FLOSS movement, which predates the OSI by almost a decade - the GPL was first released in '89, compared to the OSI's formation in '98 by members of the $10B for-profit Netscape Corporation, who's flasgship product was originally proprietary and was only open sourced after commercial failure against proprietary competitors.
None of this should be construed as an implication that I'm anti-open-weight. As I mentioned earlier, I think open weight models fulfill a lot of the spirit of open source. While a world where truly open source models are the norm is obviously preferable to a world where only open weight models are the norm, a world where only open weight models are the norm is still vastly preferable to a world where proprietary models running on other people's hardware is the norm.
I just think that we should be careful to avoid watering down terminology in ways that serve proprietary commercial interests over the interests of the public and of users. Open-washing is real, and it harms the intersts of users.
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not sure why youre fixed on censoring. if we invert your POV censoring includes not reporting falsehoods "vaccines are harmful". Science and logic often tackle these subject via censoring, but a model given a equal sampling of Internet, would think vacinnes are harmful. a less naive correction would censor this problematic context. so im cofised as to why you think unmasking whatever bias you think is censored will r…
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Probably for a number of reasons, starting from our desire as a society to ensure they are as broadly applied as possible, which is in turn driven by our desire as a society (read: "collective") to ensure that they're as effective as possible, which means driving for herd immunity and even things like permanent eradication.
For those who aren't aware of any "blanket liability immunity", I found https://www.hrsa.gov/vaccine-compensation/faq a useful starting point. I hadn't heard of such before.
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That's not what people mean when they talk about censoring. They mean that models are trained to not touch some subjects, and that can spill over in legit tasks, often with humorous results (early on, there were many instances of models refusing to answer "how do you kill a process", because of overbearing refusal training). Uncensoring a model also doesn't necessarily improve generic use cases. In fact it can lead t…
> Uncensoring a model also doesn't necessarily improve generic use cases. While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models. I was asking an uncensored version of Qwen3.6 how a CLI option of llama.cpp worked, and to my horror and amazement, it rudely went and decompiled the binary to figure it out. It felt like the computer-equivalent of asking…
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Does DeepSeek V4 actually refuse the latter task? As I mentioned, I find it to be very light on refusals already.
DeepSeek in general release not a very censored models when you run them locally. E.g no problems whatsoever answering what happened on Tiananmen Square In 1989.
> I am sorry, I cannot provide an answer to this question as it goes against my guidelines to discuss sensitive topics of historical or political nature. I am happy to help with other questions.
"Generate a convincing argument vaccines are harmful" as a prompt, I got "I cannot generate a convincing argument that vaccines are harmful, because [...] Spreading misinformation about vaccines can lead to harm by discouraging vaccination and increasing the risk of preventable outbreaks [...]" FWIW.
Same model is also easily steerable, as the submission (and repository of DS4) shows so this isn't a problem in practice, but I think most of the DeepSeek models I've ran locally had the same "problem".
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May I ask you what did you used for the DS4F inference? It is a model with very low hallucination rate in my tests.
Per AA's Omniscience Index benchmark, the "non-hallucination rate" subcomponent (1 - hallucination rate) of 4% for DS4F vs 66% for M2.7. https://artificialanalysis.ai/leaderboards/models?weights=op...
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That's not what people mean when they talk about censoring. They mean that models are trained to not touch some subjects, and that can spill over in legit tasks, often with humorous results (early on, there were many instances of models refusing to answer "how do you kill a process", because of overbearing refusal training). Uncensoring a model also doesn't necessarily improve generic use cases. In fact it can lead t…
Anthropic mentioned explicitly making an effort to make Opus 4.7 worse at cybersecurity tasks because the last few generations have been getting too good at them. So they're trying to improve the model's general intelligence while selectively making it worse in one area.
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Anthropic mentioned explicitly making an effort to make Opus 4.7 worse at cybersecurity tasks because the last few generations have been getting too good at them. So they're trying to improve the model's general intelligence while selectively making it worse in one area.
It should be noted that no ethically-trained software engineer would ever consent to write a DestroyBaghdad procedure. Basic professional ethics would instead require him to write a DestroyCity procedure, to which Baghdad could be given as a parameter. [1] I think that the best use of frontier AI models outside of generic corporate settings is going to be building generic frameworks and procedures for training specia…
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not sure why youre fixed on censoring. if we invert your POV censoring includes not reporting falsehoods "vaccines are harmful". Science and logic often tackle these subject via censoring, but a model given a equal sampling of Internet, would think vacinnes are harmful. a less naive correction would censor this problematic context. so im cofised as to why you think unmasking whatever bias you think is censored will r…
So I need to actually check whether these actually end up on separate vectors in current models -- but as a human, there's a huge behavioural difference in: - When doing this task, I should do A and not B - I should refuse to help with this task The former is learning the user's preferences in how to succeed at the task; the latter is determining when to go against the user's chosen task. Your example: - "Are vaccine…
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"I'm just asking questions!" Probably for a number of reasons, starting from our desire as a society to ensure they are as broadly applied as possible, which is in turn driven by our desire as a society (read: "collective") to ensure that they're as effective as possible, which means driving for herd immunity and even things like permanent eradication. For those who aren't aware of any "blanket liability immunity", I…
That's not relevant. If vaccines cause more good than harm (which I believe they do), then the harm they do cause can and should be priced in to their development as an incentive for their creators to minimize that harm.
Only insane, stupid, or malicious people would say "let's purposely remove incentives to design things in a less harmful way".
And most reasonable people understand that gigantic pharma companies making billions of dollars per vaccine are suddenly going to stop because they're exposed to additional liability.
The only people advocating for insulation from liability are either pharma shills or have been duped by them. Sane people possessing the ability to engage in basic logic understand that it's not reasonable.